Self-organization in Communicating Groups--论文代写范文精选

在任何一种社会互动，这是一个非常常见的情况，个人通常来自不同背景、习惯、思想和文化,甚至语言。能够交流,他们应该首先同意对一组通用的术语,这些术语是什么意思，这是语言习惯的出现的开端。下面的这篇essay代写范文将进行详述。

Introduction 
In the last few decades a new scientific paradigm has been slowly emerging: complexity [Waldrop, 1992; Heylighen, 2008; Heylighen, Cilliers & Gershenson, 2007]. This paradigm departs from the reductionism, determinism and materialism of classical, Newtonian science by focusing on the non-linear interactions between the components of a complex system. Out of these interactions new properties or forms of organization emerge, a phenomenon termed selforganization. The present paper will sketch the basic ideas of the complexity paradigm, and then apply them to social systems, and in particular to groups of communicating individuals who together need to agree about how to tackle some problem or how to coordinate their actions. 

This is a very common situation in any kind of social interaction: individuals typically come to the table with different backgrounds, habits, ideas, cultures, perspectives and even languages. To be able to communicate at all, they should first agree about a common set of terms and what those terms mean. This is the emergence of linguistic conventions. Then they should agree about basic assumptions, such as what the situation is, what can be done about it, and what should be done about it. Finally, they will need to agree about who will do what when. If successful, this sequence of agreements will lead to a coordinated form of action, where the different members of the group contribute in an efficient way to a collective solution of whatever their problem was. This phenomenon, where a group of initially independent agents develop a collective approach to the tackling of some shared problem that is more powerful than the approach any of them might have developed individually, may be called collective intelligence [Heylighen 1999; Lévy, 1997]. 

The emergence of collective intelligence is intrinsically a process of self-organization. If the process were directed by a single individual (say, the group leader), who imposes a consensus view on the others, then that perspective would not be more powerful than the perspective of the leading individual. In other words, the collective would not be in any way more intelligent than its leader. Self-organization happens in a distributed or decentralized manner: the different members of the group all contribute to the emerging organization, and no one is in control. This makes the process complex and intrinsically unpredictable, as tiny differences in the initial state (such as who speaks first, or which word is initially used to designate a particular item) may lead to very different outcomes. 

That is why such a process of group discussion and emergent interaction patterns needs to be understood with the conceptual tools of complexity science. The paper will start with a short review of these concepts, contrasting them with the older, Newtonian paradigm. I will then elaborate these concepts to provide an integrated foundation for a theory of self-organization, to be understood as a non-linear process of spontaneous coordination between actions. Such coordination will be shown to consist of the following components: alignment, division of labor, workflow and aggregation. I will then review some paradigmatic simulations and experiments that illustrate the alignment of references and communicative conventions between communicating agents. Finally, the paper will summarize the preliminary results of a series of experiments that I devised in order to – 2 – observe the emergence of collective intelligence within a communicating group, and interpret these observations in terms of alignment, division of labor and workflow.

Complex Systems 
Classical science, as exemplified by Newtonian mechanics, is essentially reductionist: it reduces all complex phenomena to their simplest components, and then tries to describe these components in a complete, objective and deterministic manner [Prigogine & Stengers, 1984; Gershenson & Heylighen, 2005; Heylighen, Cilliers & Gershenson, 2007]. The philosophy of complexity is that this is in general impossible: complex systems, such as organisms, societies, languages, or the Internet, have properties—emergent properties—that cannot be reduced to the mere properties of their parts. Moreover, the behavior of these systems has aspects that are intrinsically unpredictable and uncontrollable, and that cannot be described in any complete manner. Finally, Newtonian mechanics assumes that all changes are reversible, and therefore that there is no fundamental difference between the past and the future. Complex systems, on the other hand, are characterized by an irreversible evolution, by an “arrow of time” that points unambiguously from the past to the future, and that allows no turning back [Prigogine & Stengers, 1984]. 

While these observations are mostly negative, emphasizing the traditional qualities that complex systems lack, complex systems also have a number of surprisingly positive features, such as adaptivity, autonomy and robustness, that traditional mechanistic systems lack. These qualities can all be seen as aspects of the process of self-organization that typifies complex systems: these systems spontaneously organize themselves so as to better cope with various internal and external problems and perturbations. This allows them to evolve and adapt to a constantly changing environment. Thus, the arrow of time tends to point towards an improved, better organized or more adapted version of the evolving system [Stewart, 2000]. This adaptive organization produced by self-organizing evolution can be seen as a form of knowledge or intelligence: the system has become better at solving the problems that confront it; it now “knows” what to do when confronted with a perturbation [Heylighen, 2007b]. 

More fundamentally, the complex systems approach has done away with the old philosophy of dualism, which sees the world as made out of two distinct substances: matter, as described by the natural sciences, and mind, as described by the social sciences and humanities. In the systems approach, matter and mind are merely two different aspects of the same basic phenomenon of organization, with matter representing the simple, static, passive, causally determined aspects, and mind the more complex, dynamic, active, goal-directed aspects. As systems evolve, starting from elementary particles via atoms, molecules and organisms to brains, societies, languages and cultures, they become more complex and adaptive, and therefore more “mind-like” and less “matter-like”. 

However, that does not mean that mind should be understood merely as a complex arrangement of pieces of matter: the material components themselves can already be conceptualized as having rudimentary “mind-like” qualities, such as sensitivity, intention, and action [Heylighen, 2011]. For example, a molecule may sense the presence of another molecule and act upon that molecule via electromagnetic interaction between the charged atoms in the molecule. Its implicit “goal” or “intention” in that interaction is to find a configuration that minimizes its potential energy.

Self-organization 
The concept of self-organization is becoming increasingly popular in various branches of science and technology. Although there is no generally accepted definition [Gershenson & Heylighen, 2003], a self-organizing system may be characterized by global, coordinated activity arising spontaneously from local interactions between the system's components or “agents”. This activity is distributed over all components, without a central controller supervising or directing the behavior. For example, in a school of fish each individual fish bases its behavior on its perception of the position and speed of its immediate neighbors, rather than on the behavior of a “central fish” or that of the whole school. 

Self-organization establishes a relation between the behavior of the individual components and the structure and functionality of the system as a whole: simple interactions at the local level give rise to complex patterns at the global level. This phenomenon is called emergence. – 4 – The term “self-organization” was first proposed by the cybernetician Ashby [Ashby, 1947]. He noted that a dynamic system left on its own will spontaneously evolve towards what we now call an “attractor”: a stable regime of activity towards which the system will tend to return even if disturbed. He further noted that in this regime the different components of the system are in a sense mutually adapted, so that they function in a coordinated, “organized” manner. In 1960, the first conference on self-organizing systems was organized [Yovitts & Cameron, 1960]. One of the contributors, von Foerster [1960], formulated another fundamental mechanism: the “order from noise” principle, which notes that the more random variation (noise) the system is subjected to, the faster it will self-organize (create order). 

A similar principle, “order through fluctuations”, was formulated a couple of years later by the Nobel-prize winning chemist Prigogine [Nicolis & Prigogine, 1977], who applied selforganization to explain the “dissipative structures” that appear in thermodynamic systems far from equilibrium. In the same period, the physicist Haken [1977] founded the domain of synergetics, a mathematical approach towards understanding the spontaneous cooperation that emerges in systems with many components, as exemplified by lasers and phase transitions. Another early application of self-organizing mechanisms were neural networks: computer simulations of how the neurons in the brain perform complex tasks (such as learning, classification, and pattern recognition) in a very robust manner without centralized control.

Conclusion 
This paper has reviewed the mechanism of self-organization, conceived as the spontaneous coordination of actions performed by different agents. Such coordination helps to make the actions more synergetic, while reducing the friction between them. The result is that coordinated actions achieve their intended goals more easily and more effectively. In particular, coordination may result in an apparently unreachable goal or unsolvable problem getting within reach, as sources of obstruction vanish and missing elements are fitted in. The underlying dynamic of self-organization is local trial-and-error or variation-andselection, in which two interacting agents try to mutually adapt their actions, until they hit on a “coordinated” pattern that is acceptable to both, and thus is selectively retained. 

This local pattern is then typically propagated step-by-step to the neighboring agents and the neighbors’ neighbors. The spreading “wave” of coordination is thus amplified until it encompasses the global system, via a process of positive feedback. Coordination can be decomposed into four relatively independent mechanisms: alignment, division of labor, workflow and aggregation. Alignment is the simplest, as it merely requires the agents to “point in the same direction”, i.e. direct their actions at the same targets. This is necessary to avoid the friction that is otherwise caused by opposing actions. Alignment creates convergence or homogeneity between interacting agents. When the agents are distributed across space, the resulting homogeneity may be limited to a certain region, with boundaries between differently aligned regions emerging after self-organization. This can explain the appearance of separate cultures or languages, and the points of friction between them. （essay代写)

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